Correct, don't draw.
Annotgrove pre-labels your computer-vision training set. Your team fixes bounding boxes instead of drawing from scratch, cutting weeks off the path from raw data to a shippable model checkpoint.
From raw frames to training-ready labels in three steps
No annotation tool onboarding. No human-from-scratch drawing. Upload, run, and correct.
Upload raw frames
Push images via REST API or drag-drop. Supports JPEG, PNG, TIFF at any resolution. Batch ingest up to 50,000 frames at once.
AI pre-labels at 91% accuracy
Our detection model runs across your dataset, generating bounding boxes, masks, or keypoints. Average accuracy: 91% on COCO-style benchmarks.
Your team corrects edge cases
Reviewers fix the 9%, not draw the 100%. Export to COCO JSON, YOLO, or Pascal VOC when done. Feed directly into your training run.
Numbers from teams already using Annotgrove
Built for teams that ship models, not annotations
Everything your pipeline needs from pre-label to export.
Pre-labeling: bbox, polygon, keypoint
One AI run handles bounding boxes, semantic masks, and skeletal keypoint annotation. Your annotators review only the model's low-confidence predictions.
Quality consensus scoring
Track inter-annotator agreement on every label. Surface systematic disagreement before it corrupts your training data and tanks model accuracy.
Model-in-the-loop review
Your model flags the frames it's least confident on. Annotators focus correction effort where it moves the accuracy needle, not on uniform sampling.
Direct export: COCO, YOLO, Pascal VOC
Export with a single API call. Validated output format, no manual JSON editing. Plug directly into PyTorch, TensorFlow, or HuggingFace training scripts.
Works with the tools your team already uses
Annotgrove connects to every major part of the ML stack, no new toolchain required.
What teams say after their first dataset
We used to spend two weeks per dataset. Now our team corrects a full checkpoint's worth in three days. Pre-labeling changed what iteration speed means for us.
Pre-label accuracy on our medical images was high enough that we only corrected about one box in five. That is not what I expected when we started the pilot.
Start correcting instead of drawing
Free plan covers 10,000 labels per month. No credit card required. Upgrade when your datasets do.